def test_diag(dtype, num_charges):
np.random.seed(10)
backend = symmetric_backend.SymmetricBackend()
a = get_tensor(3, num_charges, dtype)
with pytest.raises(ValueError):
backend.diag(a)
b = get_chargearray(num_charges, dtype)
expected = diag(b)
actual = backend.diag(b)
np.testing.assert_allclose(expected.data, actual.data)
assert np.all([
charge_equal(expected._charges[n], actual._charges[n])
for n in range(len(actual._charges))
])
def test_svds(dtype, R, R1, num_charges):
np.random.seed(10)
D = 30
charges = [
BaseCharge(np.random.randint(-5, 6, (D, num_charges)),
charge_types=[U1Charge] * num_charges) for n in range(R)
]
flows = [True] * R
A = BlockSparseTensor.random(
[Index(charges[n], flows[n]) for n in range(R)], dtype=dtype)
u, s, v, _ = decompositions.svd(bs, A, R1)
u_dense, s_dense, v_dense, _ = np_decompositions.svd(np, A.todense(), R1)
res1 = bs.tensordot(bs.tensordot(u, bs.diag(s), 1), v, 1)
res2 = np.tensordot(np.tensordot(u_dense, np.diag(s_dense), 1), v_dense, 1)
np.testing.assert_almost_equal(res1.todense(), res2)
def test_max_singular_values_larger_than_bond_dimension(dtype, num_charges):
np.random.seed(10)
R = 2
D = 30
charges = [
BaseCharge(np.random.randint(-5, 6, (D, num_charges)),
charge_types=[U1Charge] * num_charges) for n in range(R)
]
flows = [True] * R
random_matrix = BlockSparseTensor.random(
[Index(charges[n], flows[n]) for n in range(R)], dtype=dtype)
U, S, V = bs.svd(random_matrix, full_matrices=False)
S.data = np.array(range(len(S.data)))
val = U @ bs.diag(S) @ V
_, S2, _, _ = decompositions.svd(bs, val, 1, max_singular_values=40)
assert S2.shape == S.shape
def test_diagflat(dtype, num_charges):
np.random.seed(10)
backend = symmetric_backend.SymmetricBackend()
a = get_tensor(3, num_charges, dtype)
with pytest.raises(ValueError):
backend.diagflat(a)
b = get_chargearray(num_charges, dtype)
expected = diag(b)
actual = backend.diagflat(b)
np.testing.assert_allclose(expected.data, actual.data)
assert np.all([
charge_equal(expected._charges[n], actual._charges[n])
for n in range(len(actual._charges))
])
with pytest.raises(NotImplementedError,
match="Can't specify k with Symmetric backend"):
actual = backend.diagflat(b, k=1)
def test_max_truncation_error(dtype, num_charges):
np.random.seed(10)
R = 2
D = 30
charges = [
BaseCharge(np.random.randint(-5, 6, (D, num_charges)),
charge_types=[U1Charge] * num_charges) for n in range(R)
]
flows = [True] * R
random_matrix = BlockSparseTensor.random(
[Index(charges[n], flows[n]) for n in range(R)], dtype=dtype)
U, S, V = bs.svd(random_matrix, full_matrices=False)
svals = np.array(range(1, len(S.data) + 1)).astype(np.float64)
S.data = svals[::-1]
val = U @ bs.diag(S) @ V
trunc = 8
mask = np.sqrt(np.cumsum(np.square(svals))) >= trunc
_, S2, _, _ = decompositions.svd(bs, val, 1, max_truncation_error=trunc)
np.testing.assert_allclose(S2.data, svals[mask][::-1])
